




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__A								=       '''bert-base-cased'''
__A								=       '''google/pegasus-xsum'''
__A								=       [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__A								=       ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__A								=       '''patrickvonplaten/t5-tiny-random'''
__A								=       '''sshleifer/bart-tiny-random'''
__A								=       '''sshleifer/tiny-mbart'''
__A								=       '''sshleifer/tiny-marian-en-de'''







def      lowerCamelCase_						(			UpperCamelCase__							: Path					,	UpperCamelCase__							: list )	->			Dict:





				"""simple docstring"""


				__lowerCamelCase									=       '\n'.join(UpperCamelCase_ )
				Path(UpperCamelCase_ ).open('w' ).writelines(UpperCamelCase_ )







def      lowerCamelCase_						(			UpperCamelCase__							: List[Any] )	->			List[Any]:





				"""simple docstring"""


				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(UpperCamelCase_					,	F"""{split}.source""" )					,	UpperCamelCase_ )
								_dump_articles(os.path.join(UpperCamelCase_					,	F"""{split}.target""" )					,	UpperCamelCase_ )
				return tmp_dir







class        __lowerCAmelCase					(			snake_case__					):

			"""simple docstring"""







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]     ,		)
			@slow
			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       AutoTokenizer.from_pretrained(_UpperCAmelCase				)
							__lowerCamelCase									=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
							__lowerCamelCase									=       max(len(tokenizer.encode(_UpperCAmelCase				)				) for a in ARTICLES				)
							__lowerCamelCase									=       max(len(tokenizer.encode(_UpperCAmelCase				)				) for a in SUMMARIES				)
							__lowerCamelCase									=       4
							__lowerCamelCase									=       8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							__lowerCamelCase			,		__lowerCamelCase									=       'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
							__lowerCamelCase									=       SeqaSeqDataset(
							    _UpperCAmelCase     ,		data_dir=_UpperCAmelCase     ,		type_path='train'     ,		max_source_length=_UpperCAmelCase     ,		max_target_length=_UpperCAmelCase     ,		src_lang=_UpperCAmelCase     ,		tgt_lang=_UpperCAmelCase     ,		)
							__lowerCamelCase									=       DataLoader(_UpperCAmelCase     ,		batch_size=2     ,		collate_fn=train_dataset.collate_fn				)
							for batch in dataloader:
											assert isinstance(_UpperCAmelCase     ,		_UpperCAmelCase				)
											assert batch["attention_mask"].shape == batch["input_ids"].shape
											# show that articles were trimmed.
											assert batch["input_ids"].shape[1] == max_src_len
											# show that targets are the same len
											assert batch["labels"].shape[1] == max_tgt_len
											if tok_name != MBART_TINY:
															continue
											# check language codes in correct place
											__lowerCamelCase									=       shift_tokens_right(batch['labels']     ,		tokenizer.pad_token_id				)
											assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
											assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
											assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
											assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

											break  # No need to test every batch


			@parameterized.expand([BART_TINY, BERT_BASE_CASED]				)
			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       AutoTokenizer.from_pretrained(_UpperCAmelCase				)
							__lowerCamelCase									=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
							__lowerCamelCase									=       max(len(tokenizer.encode(_UpperCAmelCase				)				) for a in ARTICLES				)
							__lowerCamelCase									=       max(len(tokenizer.encode(_UpperCAmelCase				)				) for a in SUMMARIES				)
							__lowerCamelCase									=       4
							__lowerCamelCase									=       LegacySeqaSeqDataset(
							    _UpperCAmelCase     ,		data_dir=_UpperCAmelCase     ,		type_path='train'     ,		max_source_length=20     ,		max_target_length=_UpperCAmelCase     ,		)
							__lowerCamelCase									=       DataLoader(_UpperCAmelCase     ,		batch_size=2     ,		collate_fn=train_dataset.collate_fn				)
							for batch in dataloader:
											assert batch["attention_mask"].shape == batch["input_ids"].shape
											# show that articles were trimmed.
											assert batch["input_ids"].shape[1] == max_len_source
											assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
											# show that targets were truncated
											assert batch["labels"].shape[1] == trunc_target  # Truncated
											assert max_len_target > trunc_target  # Truncated
											break  # No need to test every batch


			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'				)

							__lowerCamelCase									=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
							__lowerCamelCase									=       tmp_dir.joinpath('train.source'				).open().readlines()
							__lowerCamelCase									=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
							pack_data_dir(_UpperCAmelCase     ,		_UpperCAmelCase     ,		128     ,		_UpperCAmelCase				)
							__lowerCamelCase									=       {x.name for x in tmp_dir.iterdir()}
							__lowerCamelCase									=       {x.name for x in save_dir.iterdir()}
							__lowerCamelCase									=       save_dir.joinpath('train.source'				).open().readlines()
							# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
							# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
							assert len(_UpperCAmelCase				) < len(_UpperCAmelCase				)
							assert len(_UpperCAmelCase				) == 1
							assert len(packed_examples[0]				) == sum(len(_UpperCAmelCase				) for x in orig_examples				)
							assert orig_paths == new_paths


			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE     ,		reason='This test requires fairseq'				)
			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							if not FAIRSEQ_AVAILABLE:
											return
							__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       self._get_dataset(max_len=64				)
							__lowerCamelCase									=       64
							__lowerCamelCase									=       ds.make_dynamic_sampler(_UpperCAmelCase     ,		required_batch_size_multiple=_UpperCAmelCase				)
							__lowerCamelCase									=       [len(_UpperCAmelCase				) for x in batch_sampler]
							assert len(set(_UpperCAmelCase				)				) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(_UpperCAmelCase				) == len(_UpperCAmelCase				)  # no dropped or added examples
							__lowerCamelCase									=       DataLoader(_UpperCAmelCase     ,		batch_sampler=_UpperCAmelCase     ,		collate_fn=ds.collate_fn     ,		num_workers=2				)
							__lowerCamelCase									=       []
							__lowerCamelCase									=       []
							for batch in data_loader:
											__lowerCamelCase									=       batch['input_ids'].shape
											__lowerCamelCase									=       src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											__lowerCamelCase									=       np.product(batch['input_ids'].shape				)
											num_src_per_batch.append(_UpperCAmelCase				)
											if num_src_tokens > (max_tokens * 1.1):
															failures.append(_UpperCAmelCase				)
							assert num_src_per_batch[0] == max(_UpperCAmelCase				)
							if failures:
											raise AssertionError(f"""too many tokens in {len(_UpperCAmelCase				)} batches"""				)


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       self._get_dataset(max_len=512				)
							__lowerCamelCase									=       2
							__lowerCamelCase									=       ds.make_sortish_sampler(_UpperCAmelCase     ,		shuffle=_UpperCAmelCase				)

							__lowerCamelCase									=       DataLoader(_UpperCAmelCase     ,		batch_size=_UpperCAmelCase     ,		collate_fn=ds.collate_fn     ,		num_workers=2				)
							__lowerCamelCase									=       DataLoader(_UpperCAmelCase     ,		batch_size=_UpperCAmelCase     ,		collate_fn=ds.collate_fn     ,		num_workers=2     ,		sampler=_UpperCAmelCase				)

							__lowerCamelCase									=       tokenizer.pad_token_id

							def count_pad_tokens(lowerCamelCase__     ,		lowerCamelCase__="input_ids"				):
											return [batch[k].eq(_UpperCAmelCase				).sum().item() for batch in data_loader]

							assert sum(count_pad_tokens(_UpperCAmelCase     ,		k='labels'				)				) < sum(count_pad_tokens(_UpperCAmelCase     ,		k='labels'				)				)
							assert sum(count_pad_tokens(_UpperCAmelCase				)				) < sum(count_pad_tokens(_UpperCAmelCase				)				)
							assert len(_UpperCAmelCase				) == len(_UpperCAmelCase				)


			def  lowercase_       ( self     ,		lowerCamelCase__=1_000     ,		lowerCamelCase__=128				)       -> Dict:



							'''simple docstring'''
							if os.getenv('USE_REAL_DATA'     ,		_UpperCAmelCase				):
											__lowerCamelCase									=       'examples/seq2seq/wmt_en_ro'
											__lowerCamelCase									=       max_len * 2 * 64
											if not Path(_UpperCAmelCase				).joinpath('train.len'				).exists():
															save_len_file(_UpperCAmelCase     ,		_UpperCAmelCase				)
							else:
											__lowerCamelCase									=       'examples/seq2seq/test_data/wmt_en_ro'
											__lowerCamelCase									=       max_len * 4
											save_len_file(_UpperCAmelCase     ,		_UpperCAmelCase				)

							__lowerCamelCase									=       AutoTokenizer.from_pretrained(_UpperCAmelCase				)
							__lowerCamelCase									=       SeqaSeqDataset(
							    _UpperCAmelCase     ,		data_dir=_UpperCAmelCase     ,		type_path='train'     ,		max_source_length=_UpperCAmelCase     ,		max_target_length=_UpperCAmelCase     ,		n_obs=_UpperCAmelCase     ,		)
							return ds, max_tokens, tokenizer


			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       self._get_dataset()
							__lowerCamelCase									=       set(DistributedSortishSampler(_UpperCAmelCase     ,		256     ,		num_replicas=2     ,		rank=0     ,		add_extra_examples=_UpperCAmelCase				)				)
							__lowerCamelCase									=       set(DistributedSortishSampler(_UpperCAmelCase     ,		256     ,		num_replicas=2     ,		rank=1     ,		add_extra_examples=_UpperCAmelCase				)				)
							assert idsa.intersection(_UpperCAmelCase				) == set()






			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]     ,		)
			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       AutoTokenizer.from_pretrained(_UpperCAmelCase     ,		use_fast=_UpperCAmelCase				)
							if tok_name == MBART_TINY:
											__lowerCamelCase									=       SeqaSeqDataset(
											    _UpperCAmelCase     ,		data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)     ,		type_path='train'     ,		max_source_length=4     ,		max_target_length=8     ,		src_lang='EN'     ,		tgt_lang='FR'     ,		)
											__lowerCamelCase									=       train_dataset.dataset_kwargs
											assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
											__lowerCamelCase									=       SeqaSeqDataset(
											    _UpperCAmelCase     ,		data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)     ,		type_path='train'     ,		max_source_length=4     ,		max_target_length=8     ,		)
											__lowerCamelCase									=       train_dataset.dataset_kwargs
											assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
											assert len(_UpperCAmelCase				) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase				) == 0







'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							a__:
			a_	: CommonSchedulerState

			# setable values
			a_	: jnp.ndarray
			a_	: jnp.ndarray
			a_	: Optional[int]									=      None






			@classmethod
			def 						_lowercase  (     cls					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Dict:
					return cls(common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase						)





@dataclass
class 							a__( snake_case__     ):
			a_	: DDPMSchedulerState





class 							a__( snake_case__   ,      snake_case__     ):
			a_	: Union[str, Any]									=      [e.name for e in FlaxKarrasDiffusionSchedulers]

			a_	: jnp.dtype






			@property
			def 						_lowercase  (     self						)							->							Union[str, Any]:
					return True






			@register_to_config
			def __init__(     self					,   _UpperCAmelCase = 1000					,   _UpperCAmelCase = 0.0_001					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = "linear"					,   _UpperCAmelCase = None					,   _UpperCAmelCase = "fixed_small"					,   _UpperCAmelCase = True					,   _UpperCAmelCase = "epsilon"					,   _UpperCAmelCase = jnp.floataa					,   )							->							Union[str, Any]:
					snake_case__	     =dtype






			def 						_lowercase  (     self					,   _UpperCAmelCase = None						)							->							DDPMSchedulerState:
					if common is None:
							snake_case__	     =CommonSchedulerState.create(self						)

					# standard deviation of the initial noise distribution
					snake_case__	     =jnp.array(1.0					,   dtype=self.dtype						)

					snake_case__	     =jnp.arange(0					,   self.config.num_train_timesteps						).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_UpperCAmelCase					,   init_noise_sigma=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							jnp.ndarray:
					return sample






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = ()						)							->							DDPMSchedulerState:
					snake_case__	     =self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					snake_case__	     =(jnp.arange(0					,   _UpperCAmelCase						) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_UpperCAmelCase					,   timesteps=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None						)							->							Optional[Any]:
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)

					# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
					# and sample from it to get previous sample
					# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
					snake_case__	     =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
							snake_case__	     =self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
							snake_case__	     =jnp.log(jnp.clip(_UpperCAmelCase					,   a_min=1E-20						)						)
					elif variance_type == "fixed_large":
							snake_case__	     =state.common.betas[t]
					elif variance_type == "fixed_large_log":
							# Glide max_log
							snake_case__	     =jnp.log(state.common.betas[t]						)
					elif variance_type == "learned":
							return predicted_variance
					elif variance_type == "learned_range":
							snake_case__	     =variance
							snake_case__	     =state.common.betas[t]
							snake_case__	     =(predicted_variance + 1) / 2
							snake_case__	     =frac * max_log + (1 - frac) * min_log

					return variance






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = None					,   _UpperCAmelCase = True					,   )							->							Union[FlaxDDPMSchedulerOutput, Tuple]:
					snake_case__	     =timestep

					if key is None:
							snake_case__	     =jax.random.PRNGKey(0						)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
							snake_case__     ,	snake_case__	     =jnp.split(_UpperCAmelCase					,   sample.shape[1]					,   axis=1						)
					else:
							snake_case__	     =None

					# 1. compute alphas, betas
					snake_case__	     =state.common.alphas_cumprod[t]
					snake_case__	     =jnp.where(t > 0					,   state.common.alphas_cumprod[t - 1]					,   jnp.array(1.0					,   dtype=self.dtype						)						)
					snake_case__	     =1 - alpha_prod_t
					snake_case__	     =1 - alpha_prod_t_prev

					# 2. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
					if self.config.prediction_type == "epsilon":
							snake_case__	     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
							snake_case__	     =model_output
					elif self.config.prediction_type == "v_prediction":
							snake_case__	     =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
							raise ValueError(
							    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
							    ' for the FlaxDDPMScheduler.'						)

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
							snake_case__	     =jnp.clip(_UpperCAmelCase					,   -1					,   1						)

					# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					snake_case__	     =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

					# 5. Compute predicted previous sample µ_t
					# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
					snake_case__	     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
							snake_case__	     =jax.random.split(_UpperCAmelCase					,   num=1						)
							snake_case__	     =jax.random.normal(_UpperCAmelCase					,   shape=model_output.shape					,   dtype=self.dtype						)
							return (self._get_variance(_UpperCAmelCase					,   _UpperCAmelCase					,   predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

					snake_case__	     =jnp.where(t > 0					,   random_variance()					,   jnp.zeros(model_output.shape					,   dtype=self.dtype						)						)

					snake_case__	     =pred_prev_sample + variance

					if not return_dict:
							return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase					,   state=_UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return add_noise_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   )							->							jnp.ndarray:
					return get_velocity_common(state.common					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase						)






			def __len__(     self						)							->							Optional[int]:
					return self.config.num_train_timesteps




